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can you give a few pointers on articles or examples of this?

by viksit 3y ago

minimaxir 3y ago
A low-tech example to create a good blog post title for submission to Hacker News would be a system prompt like: You are an expert copywriter. Write five distinct blog post titles optimized for high clickthrough for Hacker News for the article the user provides. Your response must follow the style of these titles: - The ü/ü Conundrum - Why isn't preprint review being adopted? - Majority of web apps could just run on a single server - Weather Planning for Eclipse Day - PSChess – A chess engine in PostScript Then provide the blog post as the user message input. I just ran one of my blog posts (https://news.ycombinator.com/item?id=39495476 https://news.ycombinator.com/item?id=39495476) with the workflow through Claude Haiku and got this: Here are five distinct blog post titles optimized for high clickthrough on Hacker News for the article provided: 1. Tipping ChatGPT: Does Offering Monetary Incentives Improve AI Text Generation? 2. Quantifying the Impact of Incentives on Large Language Model Performance 3. Carrot or Stick? Exploring the Effects of Positive and Negative Prompts on ChatGPT 4. Gamifying AI: Using "Generation Golf" to Test ChatGPT's Ability to Follow Length Constraints 5. The Curious Case of ChatGPT's Motivations: Can an AI Be Incentivized Like Humans? Not bad titles, although more verbose than the 5 input examples I gave. I only gave 5 for simplicity: my main point is that you can give it a lot more than five and/or be more aggressive with constraints, like the blog post linked incidentially.
- viksit 3y ago
  interesting thank you. intuitively, prompting like this to get an answer seems basically like the first part of a fine tuning process (more exemplars). what is your thought here behind why reinforcing good output via a loss optimization is worse than the one shot example? does the model start to over fit at some point towards some local minima? and this is avoided in this scenario?
  - minimaxir 3y ago
    Prompt engineering in general is necessary because LLMs optimize for the average output, and average output is not good. So LLMs need a slight nudge.
netdur 3y ago
Use Gemini 1.5 Pro, which has 1.5 million tokens. Prompt it with a logical question and observe it struggling to answer. Then, upload a book on logical thinking in PDF format and ask the same question again. Notice how it can now answer the question effectively.

can you give a few pointers on articles or examples of this?

by viksit 3y ago

can you give a few pointers on articles or examples of this?

A low-tech example to create a good blog post title for submission to Hacker News would be a system prompt like: You are an expert copywriter. Write five distinct blog post titles optimized for high clickthrough for Hacker News for the article the user provides. Your response must follow the style of these titles: - The ü/ü Conundrum - Why isn't preprint review being adopted? - Majority of web apps could just run on a single server - Weather Planning for Eclipse Day - PSChess – A chess engine in PostScript Then provide the blog post as the user message input. I just ran one of my blog posts (https://news.ycombinator.com/item?id=39495476 https://news.ycombinator.com/item?id=39495476) with the workflow through Claude Haiku and got this: Here are five distinct blog post titles optimized for high clickthrough on Hacker News for the article provided: 1. Tipping ChatGPT: Does Offering Monetary Incentives Improve AI Text Generation? 2. Quantifying the Impact of Incentives on Large Language Model Performance 3. Carrot or Stick? Exploring the Effects of Positive and Negative Prompts on ChatGPT 4. Gamifying AI: Using "Generation Golf" to Test ChatGPT's Ability to Follow Length Constraints 5. The Curious Case of ChatGPT's Motivations: Can an AI Be Incentivized Like Humans? Not bad titles, although more verbose than the 5 input examples I gave. I only gave 5 for simplicity: my main point is that you can give it a lot more than five and/or be more aggressive with constraints, like the blog post linked incidentially.

viksit 3y ago
interesting thank you. intuitively, prompting like this to get an answer seems basically like the first part of a fine tuning process (more exemplars). what is your thought here behind why reinforcing good output via a loss optimization is worse than the one shot example? does the model start to over fit at some point towards some local minima? and this is avoided in this scenario?
- minimaxir 3y ago
  Prompt engineering in general is necessary because LLMs optimize for the average output, and average output is not good. So LLMs need a slight nudge.

netdur 3y ago

Use Gemini 1.5 Pro, which has 1.5 million tokens. Prompt it with a logical question and observe it struggling to answer. Then, upload a book on logical thinking in PDF format and ask the same question again. Notice how it can now answer the question effectively.